Delve into foundational Python programming, explore libraries like pandas and Matplotlib, and gain insights into data management, analysis, and visualization with practical projects.

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Python is one of the most popular programming languages for data science and analytics. It’s used across a wide range of industries. It’s easy to learn, highly flexible, and its various libraries can expand functionality to natively perform statistical functions and plotting.

This course is a comprehensive introduction to statistical analysis using Python. You’ll start with a step-by-step guide to the fundamentals of programming in Python. You’ll learn to apply these functions to numerical data. You’ll first look at strings, lists, dictionaries, loops, functions, and data maps. After mastering these, you’ll take a deep dive through various Python libraries, including pandas, NumPy, Matplotlib, Seaborn, and Plotly. You’ll wrap up with guided projects to clean, analyze, and visualize unique datasets using these libraries.

By the end of this course, you will be proficient in data science, including data management, analysis, and visualization.

Introduction to Data Science with Python

# Function to calculate a receipt 

Let’s assume the following scenario: You’re a loyal customer at a store that’s offering the following discounts to its loyal customers:
* For a purchase between 0 and 50 CAD, you get a 10 percent discount.
* For a purchase between 51 and 100 CAD, you get a 20 percent discount.
* For a purchase of more than 100 CAD, you get a 40 percent discount.
* On Mondays, if you spend more than 50 CAD, you get an additional 10 CAD off.

For this task, you need to write a function to calculate how much the customer’s purchase will actually cost after discounts and return that value.

> **Tip:** A good way to handle this problem is to develop the logic first. Take a pen and a notebook and do the calculations by hand, if required. Test your function for `51, 50, 100, 101` using the `for` loop at the end.

## Input
The input will contain two values:

1. An integer value of the purchase cost.
2. A boolean value—either it’s Monday or not. `True` is for Monday, and `False` is not Monday.

## Output
A floating value of total cost after discounts.

## Sample input
`cost=50, is_monday=True`
## Sample output
45.0

Complete another challenge to practice what you’ve learned about Python essentials so far.

Introduction

Python Essentials

NumPy: Data Analysis

Pandas: Data Analysis

Matplotlib: Data Visualization

Seaborn: Statistical Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Pandas: Data Visualization

Data Visualization with Seaborn for Walmart Sales Projection

Plotly: Interactive Data Visualization

Project 1: Oil vs Banks During Recession

Project 2: Emergency Calls Dataset

In Closing

Challenge 2: Python Essentials

Function to calculate a receipt